What this category of data looks like
Data sources include design history files, component and material batch records, MES systems, cleanroom environment monitoring equipment, sterilization equipment, and inspection and calibration systems. Data is synchronized upon completion of each product batch or equipment unit. Cleanroom zone data is updated regularly per established monitoring plans. Daily documents cover production process specifications, batch production records, cleanroom zone operation records, sterilization batch records, inspection reports, software version records, adverse event and traceability materials. Data fields include batch number, material batch number, sterilization parameters, cleanroom zone temperature and humidity, unique device identification, inspection results, and other relevant fields. These must match the standardized format required for medical device traceability.
What constraints these characteristics impose on database and operations maintenance
Heterogeneous data from multiple sources must adapt to different write formats and validation rules. This prevents unstructured data from contaminating the database. Update cadence per batch or monitoring plan causes data volume to fluctuate with production tasks. This requires flexible adjustment of database connection resources. Data involves product traceability and aseptic control requirements. This demands strong consistency for writes and queries, to prevent data loss or tampering. Key fields such as unique device identification and sterilization parameters require strict validation. Data that does not meet requirements cannot be stored, which increases the complexity of data preprocessing.
How to set configurations
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Value Format | Rationale |
|---|---|---|
MONGODB_URI | mongodb://[username:password]@host:port/dbname?authSource=admin | Adapts to multi-source associated storage requirements for medical device ledgers, supports transactional writes and complex queries |
PG_URL | postgresql://user:pass@host:port/dbname?sslmode=require | Stores sterilization batch records and traceability data that require strong consistency, ensuring data security and efficiency of associated queries |
DB_MAX_LINK | 200–300 | Handles concurrent read/write requests from multiple production batches, avoiding exhaustion of the database connection pool |
REDIS_URL | redis://:password@host:port/0 | Caches frequently queried data from cleanroom zone monitoring, reducing load pressure on the backend database |
Three common mistakes
- Phenomenon: MongoDB connection error, database status shows failure. Cause: Incorrect
MONGODB_URIconfiguration, missing correct authentication source parameter, or network policies restricting access to database ports. - Phenomenon: No response for more than 60 concurrent requests, local model server has not reached performance limits. Cause:
DB_MAX_LINKis set too low, cannot support concurrent ledger data read/write requests, leading to exhaustion of the connection pool. - Phenomenon: Missing fields or chaotic formatting in parsed PDF ledger data. Cause: No parsing rules configured for the specialized document structure of medical devices, leading to failure to correctly extract key fields such as sterilization parameters and unique device identification.
How to confirm configurations are correct
- Run database connection test commands to verify connectivity for
MONGODB_URIandPG_URL, and check the available connection count status of the connection pool. - Simulate multiple concurrent requests, observe whether the database connection count stays within the range specified by
DB_MAX_LINK, with no connection timeout errors. - Upload a typical sterilization batch record PDF document, check that the extracted fields include key information such as batch number, sterilization time, and cleanroom zone temperature and humidity.
- View the running status of the cache service corresponding to
REDIS_URL, confirm that frequently queried data is cached normally and no data is lost.
The values provided are common starting points and should be measured against the reader's own samples.